Deep Learning for Sign Language Recognition: A Comparative Review

Sign language can be regarded as a unique form of communication method between human beings, which relies basically on visualized gestures of the individual body parts to transfer messages and obtains a substantial role in the life of impaired people having hearing and speaking disabilities deaf. There are various different signs in every sign language with differences in representation using hand shape, motion type, and location of the hand, face, and body portions participate in every sign. Understanding sign language by individuals without disabilities is a challenging operation. Therefore, automated sign language recognition has become a significant need to bridge the communication gap and facilitate the interaction between the deaf society, and the normal hearing majority. In this work, an extensive review of automated sign language recognition and translation of different languages around the world has been conducted. More than 140 research articles have been reviewed, and all of them are relying on deep learning techniques, which were published between 2018 and 2022, to recognize, and translate sign language. A brief review of concepts related to sign language is also presented including its types, and acquiring methods, as well as an introduction to deep learning, and the main challenges facing the recognition process. A description of the various types of public datasets of sign language in different languages is also presented and discussed.